Introduction to ggplot2
Thomas Carroll
MRC-CSC/Shared-bioinformatics-training
Graphics in R
The R language has extensive graphical capabilities.
Graphics in R may be created by many different methods including base graphics and more advanced plotting packages such as lattice.
The ggplot2 package was created by Hadley Wickham and provides a intuitive plotting system to rapidly generate publication quality graphics.
ggplot2 builds on the concept of the “Grammar of Graphics” (Wilkinson 2005, Bertin 1983) which describes a consistent syntax for the construction of a wide range of complex graphics by a concise description of their components.
Why use ggplot2
The structured syntax and high level of abstraction used by ggplot2 should allow for the user to concentrate on the visualisations instead of creating the underlying code.
On top of this central philosophy ggplot2 has:
- Increased flexible over many plotting systems.
- An advanced theme system for professional/publication level graphics.
- Large developer base – Many libraries extending its flexibility.
- Large user base – Great documentation and active mailing list.
Grammar of Graphics
How to build a plot from its components.
How ggplot2 builds a plot.
Overview of example code for the ggplot2 scatter plot.
ggplot(data = <default data set>,
aes(x = <default x axis variable>,
y = <default y axis variable>,
... <other default aesthetic mappings>),
... <other plot defaults>) +
geom_scatter(aes(size = <size variable for this geom>,
... <other aesthetic mappings>),
data = <data for this point geom>,
stat = <statistic string or function>,
position = <position string or function>,
color = <"fixed color specification">,
<other arguments, possibly passed to the _stat_ function) +
scale_<aesthetic>_<type>(name = <"scale label">,
breaks = <where to put tick marks>,
labels = <labels for tick marks>,
... <other options for the scale>) +
ggtitle("Graphics/Plot")+
xlab("Weight")+
ylab("Height")+
theme(plot.title = element_text(colour = "gray"),
... <other theme elements>)What users are required to specify in ggplot2 to build a plot.
Actual code for the ggplot2 scatter plot.
ggplot(data=patients_clean,
aes(y=Weight,x=Height,colour=Sex,
size=BMI,shape=Pet))
+geom_point()Getting started with ggplot2
Earlier we have been working with the patients’ dataset to create a “clean and tidy” dataset.
Now we will use a cleaned dataset to demonstrate some of the plotting capabilities of ggplot2.
library(tidyr)
library(ggplot2)
library(dplyr)
library(stringr)
library(lubridate)
patients_clean <- read.delim("patient-data-cleaned.txt",sep="\t")
patients_clean## ID Name Race Sex Smokes Height Weight
## 1 AC/AH/001 Demetrius White Male Non-Smoker 182.87 76.57
## 2 AC/AH/017 Rosario White Male Non-Smoker 179.12 80.43
## 3 AC/AH/020 Julio Black Male Non-Smoker 169.15 75.48
## 4 AC/AH/022 Lupe White Male Non-Smoker 175.66 94.54
## 5 AC/AH/029 Lavern White Female Non-Smoker 164.47 71.78
## 6 AC/AH/033 Bernie <NA> Female Smoker 158.27 69.90
## 7 AC/AH/037 Samuel White Female Non-Smoker 161.69 68.85
## 8 AC/AH/044 Clair White Female Non-Smoker 165.84 70.44
## 9 AC/AH/045 Shirley White Male Non-Smoker 181.32 76.90
## 10 AC/AH/048 Merle Hispanic Male Non-Smoker 167.37 79.06
## 11 AC/AH/049 Martin White Female Non-Smoker 160.06 72.37
## 12 AC/AH/050 Frances White Female Non-Smoker 166.48 67.34
## 13 AC/AH/052 Courtney White Male Smoker 175.39 92.22
## 14 AC/AH/053 Francis White Female Smoker 164.70 75.69
## 15 AC/AH/057 Vernon White Female Smoker 163.79 65.76
## 16 AC/AH/061 Lester Black Male Non-Smoker 181.13 72.33
## 17 AC/AH/063 Robin Hispanic Male Non-Smoker 169.24 73.30
## 18 AC/AH/076 Albert White Male Non-Smoker 176.22 97.67
## 19 AC/AH/077 Tommy Black Male Non-Smoker 174.09 72.20
## 20 AC/AH/086 Kyle Black Male Smoker 180.11 75.72
## 21 AC/AH/089 Dong White Male Non-Smoker 179.24 75.54
## 22 AC/AH/100 Michel White Female Non-Smoker 161.92 69.92
## 23 AC/AH/104 Jeremy White Male Smoker 169.85 90.63
## 24 AC/AH/112 Pat Black Female Non-Smoker 160.57 63.54
## 25 AC/AH/113 Eugene White Female Non-Smoker 168.24 69.57
## 26 AC/AH/114 Kris Hispanic Male Non-Smoker 177.75 74.84
## 27 AC/AH/115 Tracy Bi-Racial Male Smoker 183.21 83.36
## 28 AC/AH/127 Jame White Male Non-Smoker 167.75 82.06
## 29 AC/AH/133 Clyde Hispanic Male Non-Smoker 181.15 83.93
## 30 AC/AH/150 Brett White Male Smoker 181.56 79.54
## 31 AC/AH/154 Tony White Female Non-Smoker 160.03 64.30
## 32 AC/AH/156 George White Male Non-Smoker 165.62 76.72
## 33 AC/AH/159 Edward White Male Non-Smoker 181.64 96.91
## 34 AC/AH/160 Rory Asian Female Non-Smoker 159.67 71.88
## 35 AC/AH/164 Shane Hispanic Male Smoker 177.03 74.04
## 36 AC/AH/171 Devin White Female Non-Smoker 163.35 70.46
## 37 AC/AH/176 Jerry Asian Male Non-Smoker 175.21 83.65
## 38 AC/AH/180 Drew White Female Non-Smoker 160.80 64.77
## 39 AC/AH/185 Ronald White Male Non-Smoker 166.46 76.83
## 40 AC/AH/186 Christopher White Female Non-Smoker 157.95 67.41
## 41 AC/AH/192 Dominique White Male Non-Smoker 180.61 83.59
## 42 AC/AH/198 Van White Female Non-Smoker 159.52 67.99
## 43 AC/AH/207 Bobbie White Female Non-Smoker 163.01 65.19
## 44 AC/AH/208 Lawrence Hispanic Female Non-Smoker 165.80 71.77
## 45 AC/AH/210 Keith Hispanic Female Smoker 170.03 66.68
## 46 AC/AH/211 Son White Female Non-Smoker 157.16 69.64
## 47 AC/AH/213 Charlie White Female Smoker 164.58 72.99
## 48 AC/AH/219 Jay White Female Non-Smoker 163.47 72.89
## 49 AC/AH/220 Richard White Male Non-Smoker 185.43 87.23
## 50 AC/AH/221 Carlos White Female Non-Smoker 165.34 70.84
## 51 AC/AH/225 Gail White Female Non-Smoker 163.45 67.67
## 52 AC/AH/233 Marion White Female Non-Smoker 163.97 66.71
## 53 AC/AH/241 Lindsay White Female Non-Smoker 161.38 73.55
## 54 AC/AH/244 Sean White Female Non-Smoker 160.09 65.93
## 55 AC/AH/248 Andrea White Male Non-Smoker 178.64 97.05
## 56 AC/AH/249 Jesus Hispanic Female Smoker 159.78 68.31
## 57 AC/SG/002 Jan White Female Smoker 161.57 67.92
## 58 AC/SG/003 Walter White Female Non-Smoker 161.83 66.03
## 59 AC/SG/008 Dana White Male Smoker 169.66 77.30
## 60 AC/SG/009 Sammy White Male Non-Smoker 166.84 88.25
## 61 AC/SG/010 Theo Asian Female Non-Smoker 159.32 64.92
## 62 AC/SG/015 Shaun White Male Smoker 170.51 84.35
## 63 AC/SG/016 Jimmie Black Female Non-Smoker 161.84 69.97
## 64 AC/SG/046 Carl Hispanic Male Non-Smoker 171.41 81.70
## 65 AC/SG/055 Evan White Male Non-Smoker 166.75 79.06
## 66 AC/SG/056 Merrill Asian Female Smoker 166.19 67.46
## 67 AC/SG/064 Jon White Male Non-Smoker 169.16 90.08
## 68 AC/SG/065 Shayne White Female Non-Smoker 157.01 66.56
## 69 AC/SG/067 Thomas White Male Non-Smoker 167.51 84.15
## 70 AC/SG/068 Valentine Hispanic Female Non-Smoker 160.47 68.20
## 71 AC/SG/072 Cameron Black Female Smoker 162.33 66.47
## 72 AC/SG/074 Eddie Hispanic Male Non-Smoker 175.67 88.82
## 73 AC/SG/084 Brian Hispanic Male Non-Smoker 174.25 80.93
## 74 AC/SG/095 Matthew White Female Non-Smoker 158.94 65.14
## 75 AC/SG/099 Leslie Asian Male Non-Smoker 172.72 67.62
## 76 AC/SG/101 Jason White Female Non-Smoker 159.23 69.96
## 77 AC/SG/107 Sol White Male Non-Smoker 176.54 90.76
## 78 AC/SG/116 Connie Black Male Non-Smoker 184.34 90.41
## 79 AC/SG/121 Rudy White Female Non-Smoker 163.94 71.47
## 80 AC/SG/122 Michal Hispanic Female Non-Smoker 160.09 68.94
## 81 AC/SG/123 Darnell White Female Smoker 162.32 72.72
## 82 AC/SG/134 Daryl White Female Smoker 162.59 69.76
## 83 AC/SG/139 Jordan White Male Non-Smoker 171.94 82.11
## 84 AC/SG/142 Kenneth White Female Non-Smoker 158.07 69.80
## 85 AC/SG/155 Raymond White Female Non-Smoker 158.35 69.72
## 86 AC/SG/165 Elmer White Female Non-Smoker 162.18 67.81
## 87 AC/SG/167 Jimmy White Female Non-Smoker 159.38 70.37
## 88 AC/SG/172 Whitney White Male Non-Smoker 171.45 84.29
## 89 AC/SG/173 Britt White Female Smoker 163.17 64.47
## 90 AC/SG/179 Logan White Male Non-Smoker 183.10 82.47
## 91 AC/SG/181 Terry Hispanic Male Non-Smoker 177.14 88.70
## 92 AC/SG/182 Jamie Hispanic Male Smoker 171.08 72.51
## 93 AC/SG/191 Lacy Hispanic Female Non-Smoker 159.33 70.68
## 94 AC/SG/193 Ronnie White Male Smoker 185.43 73.63
## 95 AC/SG/194 Joseph White Female Non-Smoker 162.65 73.99
## 96 AC/SG/197 Stacy White Female Non-Smoker 159.44 66.21
## 97 AC/SG/204 Anthony White Female Non-Smoker 164.11 70.66
## 98 AC/SG/216 Alva White Female Non-Smoker 159.13 66.96
## 99 AC/SG/217 Dean White Female Non-Smoker 160.58 71.49
## 100 AC/SG/234 Luis Hispanic Female Non-Smoker 164.88 68.07
## Birth State Pet Grade Died Count Date.Entered.Study
## 1 1972-02-06 Georgia Dog 2 FALSE 0.01 2015-12-01
## 2 1972-06-15 Missouri Dog 2 FALSE -1.31
## 3 1972-07-09 Pennsylvania None 2 FALSE -0.17
## 4 1972-08-17 Florida Cat 1 FALSE -1.10
## 5 1973-06-12 Iowa <NA> 2 TRUE 1.42
## 6 1973-07-01 Maryland Dog 2 FALSE 0.29
## 7 1972-03-26 Pennsylvania None 1 FALSE 0.16
## 8 1973-05-11 North Carolina None 1 FALSE -0.07
## 9 1971-12-31 Louisiana Dog 1 FALSE -1.43
## 10 1973-07-19 North Carolina None 2 FALSE 0.54 2015-12-31
## 11 1972-05-04 California Horse 2 TRUE -2.41
## 12 1971-11-14 Michigan None 1 FALSE 1.05
## 13 1972-03-22 Indiana Bird 3 FALSE -0.04
## 14 1971-11-22 Virginia Dog 1 FALSE -0.65
## 15 1972-01-12 Illinois Cat 3 FALSE 0.06
## 16 1972-11-22 Wisconsin Dog NA TRUE -0.15
## 17 1971-11-22 Illinois None 3 FALSE 0.68
## 18 1973-04-14 Louisiana Cat 2 FALSE -1.97
## 19 1973-02-07 Washington Cat 3 FALSE -1.18
## 20 1973-05-18 Georgia Cat 3 FALSE 0.40
## 21 1972-03-17 California None 2 TRUE -0.47
## 22 1973-01-02 Georgia Dog 1 FALSE 0.50
## 23 1972-04-18 Kentucky None 1 TRUE -0.20
## 24 1973-07-02 California <NA> NA TRUE 0.69 2016-01-31
## 25 1972-02-13 Massachusetts <NA> 2 FALSE 0.38
## 26 1972-11-25 Pennsylvania Bird 3 FALSE 0.15
## 27 1973-10-05 California Dog 2 FALSE 0.05
## 28 1972-11-04 Texas Dog 1 TRUE 0.63
## 29 1973-10-19 Washington Cat 3 TRUE -0.81 2016-03-02
## 30 1972-05-09 Kentucky Dog 1 TRUE 0.92
## 31 1973-09-05 California Dog 1 TRUE -0.10
## 32 1972-07-15 California Dog 1 TRUE 0.61
## 33 1972-12-10 Connecticut Cat 2 FALSE -0.39
## 34 1973-09-28 Florida Cat 2 TRUE 0.74
## 35 1972-02-24 Florida None 2 FALSE 0.15
## 36 1973-04-22 California Bird 3 TRUE 1.68 2016-03-31
## 37 1973-05-07 Virginia Dog 3 TRUE 0.41
## 38 1973-02-24 Oregon Cat 1 TRUE -2.17
## 39 1972-08-23 Colorado None NA TRUE -1.32
## 40 1972-05-12 New Jersey Dog 3 TRUE -1.89
## 41 1972-03-30 Michigan None 3 TRUE -0.60
## 42 1972-12-08 Missouri Cat 2 FALSE -0.40
## 43 1973-05-23 Florida Dog 2 FALSE 1.07
## 44 1973-08-13 Louisiana None 1 FALSE -1.21
## 45 1972-09-03 New York Dog NA FALSE -0.66
## 46 1973-07-20 California Cat 2 TRUE -0.70 2016-05-01
## 47 1972-01-31 Louisiana Dog 1 FALSE -0.32
## 48 1972-04-13 North Caroline Bird 1 TRUE 0.33
## 49 1973-07-19 Florida Cat 1 FALSE 1.29
## 50 1972-02-07 Michigan Dog NA TRUE 1.69
## 51 1972-10-27 Michigan Cat 2 FALSE -0.74
## 52 1971-12-29 Ohio Cat 3 TRUE -1.07
## 53 1972-02-14 Florida Cat 3 FALSE -0.82 2016-05-31
## 54 1973-01-31 Maryland None NA TRUE 0.50
## 55 1973-01-18 Indiana Cat 1 TRUE -1.21
## 56 1972-04-29 Alabama Cat 2 TRUE 0.52
## 57 1973-07-09 Arizona Dog 3 FALSE 0.33
## 58 1972-07-17 Oregon None 2 TRUE -0.90
## 59 1973-06-01 Nevada Dog 1 TRUE -2.36
## 60 1972-03-10 Vermont Dog 1 FALSE 1.51 2016-07-01
## 61 1973-02-04 New York Cat 2 TRUE 0.71
## 62 1972-11-15 New Jersey Dog 3 TRUE 0.12
## 63 1972-04-09 Arizona Cat 3 TRUE -0.57
## 64 1973-08-11 Mississippi Bird 2 TRUE 0.43
## 65 1972-03-01 Illinois Bird 3 TRUE -0.55 2016-07-31
## 66 1972-12-03 Indiana <NA> 3 TRUE -1.06
## 67 1972-10-10 Illinois Cat 2 TRUE -0.10
## 68 1972-04-11 California Dog 3 TRUE 0.64
## 69 1972-07-25 Pennsylvania Bird 2 TRUE -1.46
## 70 1972-04-21 Tennessee Cat 3 TRUE 0.83
## 71 1972-02-21 Californa <NA> 3 FALSE 1.02
## 72 1973-10-10 Georgia None NA FALSE -3.14
## 73 1972-03-12 Virginia Dog 2 TRUE 1.79
## 74 1973-06-01 Hawaii Dog 1 FALSE 0.47
## 75 1972-02-10 Ohio Cat 1 FALSE 0.05
## 76 1973-10-04 Michigan Dog 2 TRUE -0.81
## 77 1973-02-03 Hawaii None 3 FALSE -1.48 2016-08-31
## 78 1972-06-11 Florida None 3 TRUE -1.41
## 79 1973-03-18 Michigan Cat 3 FALSE -0.25
## 80 1971-12-22 South Carolina Dog 1 FALSE -0.99
## 81 1972-09-09 North Caroline Bird 1 TRUE -0.92
## 82 1972-06-03 Texas Cat 2 TRUE -0.28
## 83 1973-10-12 Michigan None 1 FALSE -1.18
## 84 1972-05-21 Kansas Dog 3 FALSE 0.54
## 85 1972-06-08 California Cat 3 TRUE 1.42
## 86 1972-03-31 Washington Bird 1 TRUE 1.68
## 87 1973-10-06 Washington None 2 TRUE 0.02 2016-10-01
## 88 1972-03-02 Florida Dog 2 TRUE -0.19
## 89 1973-06-25 Californa None 2 FALSE 0.69
## 90 1972-10-30 Ohio Dog 3 TRUE 0.77
## 91 1971-11-30 Indiana Cat 3 TRUE 1.76
## 92 1973-03-31 Louisiana None 3 TRUE -0.22
## 93 1973-06-27 Texas None 3 TRUE -1.07
## 94 1973-06-11 Iowa Dog 3 FALSE -0.29
## 95 1972-08-10 Maryland Cat 3 FALSE 0.87 2016-10-31
## 96 1972-11-14 New York Cat 1 TRUE 1.21
## 97 1972-06-23 California Dog 3 FALSE -0.17
## 98 1972-06-25 Alabama None 1 TRUE 0.95
## 99 1972-11-17 Ohio None 1 TRUE -0.78
## 100 1971-11-16 Pennsylvania Cat 3 TRUE 0.35
## Age BMI Overweight
## 1 44 22.90 FALSE
## 2 43 25.07 TRUE
## 3 43 26.38 TRUE
## 4 43 30.64 TRUE
## 5 42 26.54 TRUE
## 6 42 27.90 TRUE
## 7 44 26.34 TRUE
## 8 42 25.61 TRUE
## 9 44 23.39 FALSE
## 10 42 28.22 TRUE
## 11 44 28.25 TRUE
## 12 44 24.30 FALSE
## 13 44 29.98 TRUE
## 14 44 27.90 TRUE
## 15 44 24.51 FALSE
## 16 43 22.05 FALSE
## 17 44 25.59 TRUE
## 18 43 31.45 TRUE
## 19 43 23.82 FALSE
## 20 42 23.34 FALSE
## 21 44 23.51 FALSE
## 22 43 26.67 TRUE
## 23 44 31.42 TRUE
## 24 42 24.64 FALSE
## 25 44 24.58 FALSE
## 26 43 23.69 FALSE
## 27 42 24.83 FALSE
## 28 43 29.16 TRUE
## 29 42 25.58 TRUE
## 30 44 24.13 FALSE
## 31 42 25.11 TRUE
## 32 43 27.97 TRUE
## 33 43 29.37 TRUE
## 34 42 28.19 TRUE
## 35 44 23.63 FALSE
## 36 43 26.41 TRUE
## 37 43 27.25 TRUE
## 38 43 25.05 TRUE
## 39 43 27.73 TRUE
## 40 43 27.02 TRUE
## 41 44 25.63 TRUE
## 42 43 26.72 TRUE
## 43 42 24.53 FALSE
## 44 42 26.11 TRUE
## 45 43 23.06 FALSE
## 46 42 28.20 TRUE
## 47 44 26.95 TRUE
## 48 44 27.28 TRUE
## 49 42 25.37 TRUE
## 50 44 25.91 TRUE
## 51 43 25.33 TRUE
## 52 44 24.81 FALSE
## 53 44 28.24 TRUE
## 54 43 25.72 TRUE
## 55 43 30.41 TRUE
## 56 44 26.76 TRUE
## 57 42 26.02 TRUE
## 58 43 25.21 TRUE
## 59 42 26.85 TRUE
## 60 44 31.70 TRUE
## 61 43 25.58 TRUE
## 62 43 29.01 TRUE
## 63 44 26.71 TRUE
## 64 42 27.81 TRUE
## 65 44 28.43 TRUE
## 66 43 24.43 FALSE
## 67 43 31.48 TRUE
## 68 44 27.00 TRUE
## 69 43 29.99 TRUE
## 70 44 26.48 TRUE
## 71 44 25.22 TRUE
## 72 42 28.78 TRUE
## 73 44 26.65 TRUE
## 74 42 25.79 TRUE
## 75 44 22.67 FALSE
## 76 42 27.59 TRUE
## 77 43 29.12 TRUE
## 78 43 26.61 TRUE
## 79 43 26.59 TRUE
## 80 44 26.90 TRUE
## 81 43 27.60 TRUE
## 82 43 26.39 TRUE
## 83 42 27.77 TRUE
## 84 43 27.94 TRUE
## 85 43 27.80 TRUE
## 86 44 25.78 TRUE
## 87 42 27.70 TRUE
## 88 44 28.67 TRUE
## 89 42 24.21 FALSE
## 90 43 24.60 FALSE
## 91 44 28.27 TRUE
## 92 43 24.77 FALSE
## 93 42 27.84 TRUE
## 94 42 21.41 FALSE
## 95 43 27.97 TRUE
## 96 43 26.05 TRUE
## 97 43 26.24 TRUE
## 98 43 26.44 TRUE
## 99 43 27.72 TRUE
## 100 44 25.04 TRUEOur first ggplot2 graph
As seen above, in order to produce a ggplot2 graph we need a minimum of:-
- Data to be used in graph
- Mappings of data to the graph (aesthetic mapping)
- What type of graph we want to use (The geom to use).
In the code below we define the data as our cleaned patients data frame.
pcPlot <- ggplot(data=patients_clean)
class(pcPlot)## [1] "gg" "ggplot"pcPlot$data[1:4,]## ID Name Race Sex Smokes Height Weight Birth
## 1 AC/AH/001 Demetrius White Male Non-Smoker 182.87 76.57 1972-02-06
## 2 AC/AH/017 Rosario White Male Non-Smoker 179.12 80.43 1972-06-15
## 3 AC/AH/020 Julio Black Male Non-Smoker 169.15 75.48 1972-07-09
## 4 AC/AH/022 Lupe White Male Non-Smoker 175.66 94.54 1972-08-17
## State Pet Grade Died Count Date.Entered.Study Age BMI
## 1 Georgia Dog 2 FALSE 0.01 2015-12-01 44 22.90
## 2 Missouri Dog 2 FALSE -1.31 43 25.07
## 3 Pennsylvania None 2 FALSE -0.17 43 26.38
## 4 Florida Cat 1 FALSE -1.10 43 30.64
## Overweight
## 1 FALSE
## 2 TRUE
## 3 TRUE
## 4 TRUENow we can see that we have gg/ggplot object (pcPlot) and in this the data has been defined.
Important information on how to map the data to the visual properties (aesthetics) of the plot as well as what type of plot to use (geom) have however yet to specified.
pcPlot$mapping## * NULL ->pcPlot$theme## list()pcPlot$layers## list()The information to map the data to the plot can be added now using the aes() function.
pcPlot <- ggplot(data=patients_clean)
pcPlot <- pcPlot+aes(x=Height,y=Weight)
pcPlot$mapping## * x -> Height
## * y -> WeightpcPlot$theme## list()pcPlot$layers## list()But we are still missing the final component of our plot, the type of plot to use (geom).
Below the geom_point function is used to specify a point plot, a scatter plot of Height values on the x-axis versus Weight values on the y values.
pcPlot <- ggplot(data=patients_clean)
pcPlot <- pcPlot+aes(x=Height,y=Weight)
pcPlot <- pcPlot+geom_point()
pcPlot$mapping## * x -> Height
## * y -> WeightpcPlot$theme## list()pcPlot$layers## [[1]]
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identityNow we have all the components of our plot, we need we can display the results.
pcPlot
More typically, the data and aesthetics are defined within ggplot function and geoms applied afterwards.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight))
pcPlot+geom_point()
Geoms - Plot types
As we have seen, an important element of a ggplot is the geom used. Following the specification of data, the geom describes the type of plot used.
Several geoms are available in ggplot2:-
- geom_point() - Scatter plots
- geom_line() - Line plots
- geom_smooth() - Fitted line plots
- geom_bar() - Bar plots
- geom_boxplot() - Boxplots
- geom_jitter() - Jitter to plots
- geom_histogram() - Histogram plots
- geom_density() - Density plots
- geom_text() - Text to plots
- geom_errorbar() - Errorbars to plots
- geom_violin() - Violin plots
Geoms - Line plots
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight))
pcPlot_line <- pcPlot+geom_line()
pcPlot_line
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight))
pcPlot_smooth <- pcPlot+geom_smooth()
pcPlot_smooth## `geom_smooth()` using method = 'loess'
Geoms - Bar and frequency plots
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Sex))
pcPlot_bar <- pcPlot+geom_bar()
pcPlot_bar
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height))
pcPlot_hist <- pcPlot+geom_histogram()
pcPlot_hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height))
pcPlot_density <- pcPlot+geom_density()
pcPlot_density
Geoms - Box and violin plots
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Sex,y=Height))
pcPlot_boxplot <- pcPlot+geom_boxplot()
pcPlot_boxplot
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Sex,y=Height))
pcPlot_violin <- pcPlot+geom_violin()
pcPlot_violin
An overview of geoms and thier arguments can be found at ggplot2 documentation or within the ggplot2 cheatsheet.
Aesthetics
In order to change the property on an aesthetic of a plot into a constant value (e.g. set colour of all points to red) we can supply the colour argument to the geom_point() function.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight))
pcPlot+geom_point(colour="red")
As we discussed earlier however, ggplot2 makes use of aesthetic mappings to assign variables in the data to the properties/aesthetics of the plot. This allows the properties of the plot to reflect variables in the data dynamically.
In these examples we supply additional information to the aes() function to define what information to display and how it is represented in the plot.
First we can recreate the plot we saw earlier.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight))
pcPlot+geom_point()
Now we can adjust the aes mapping by supplying an argument to the colour parameter in the aes function. (Note that ggplot2 accepts “color” or “colour” as parameter name)
This simple adjustment allows for identifaction of the separation between male and female measurements for height and weight.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight,colour=Sex))
pcPlot+geom_point()
Similarly the shape of points may be adjusted.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight,shape=Sex))
pcPlot+geom_point()
The aesthetic mappings may be set directly in the geom_points() function as previously when specifying red. This can allow the same ggplot object to be used by different aesethetic mappings and varying geoms
pcPlot <- ggplot(data=patients_clean)
pcPlot+geom_point(aes(x=Height,y=Weight,colour=Sex))
pcPlot+geom_point(aes(x=Height,y=Weight,colour=Smokes))
pcPlot+geom_point(aes(x=Height,y=Weight,colour=Smokes,shape=Sex))
pcPlot+geom_violin(aes(x=Sex,y=Height,fill=Smokes))
Again, for a comprehensive list of parameters and aesthetic mappings used in geom_type functions see the ggplot2 documentation for individual geoms by using ?geom_type
?geom_pointor visit the ggplot2 documentations pages and cheatsheet
Facets
One very useful feature of ggplot is faceting. This allows you to produce plots subset by variables in your data.
To facet our data into multiple plots we can use the facet_wrap or facet_grid function specifying the variable we split by.
The facet_grid function is well suited to splitting the data by two factors.
Here we can plot the data with the Smokes variable as rows and Sex variable as columns.
facet_grid(Rows~Columns)
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(Smokes~Sex)
To split by one factor we can apply the facet_grid() function ommiting the variable before the “~”" to facet along columns in plot.
facet_grid(~Columns)
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(~Sex)
To split along rows in plot, the variable is placed before the “~.”.
facet_grid(Rows~.)
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(Sex~.)
The facet_wrap() function offers a less grid based structure but is well suited to faceting data by one variable.
For facet_wrap() we follow as similar syntax to facet_grid()
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_wrap(~Smokes)
For more complex faceting both facet_grid and facet_wrap can accept combinations of variables.
Using facet_wrap
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_wrap(~Pet+Smokes+Sex)
Or in a nice grid format using facet_grid() and the Smokes variable against a combination of Gender and Pet.
pcPlot + facet_grid(Smokes~Sex+Pet)
Exercise set 1
Scales
Scales and their legends have so far been handled using ggplot2 defaults. ggplot2 offers functionality to have finer control over scales and legends using the scale methods.
Scale methods are divided into functions by combinations of
the aesthetics they control.
the type of data mapped to scale.
scale_aesthetic_type
Try typing in scale_ then tab to autocomplete. This will provide some examples of the scale functions available in ggplot2.
Although different scale functions accept some variety in their arguments, common arguments to scale functions include -
name - The axis or legend title
limits - Minimum and maximum of the scale
breaks - Label/tick positions along an axis
labels - Label names at each break
Controlling the X and Y scale.
Both continous and discrete X/Y scales can be controlled in ggplot2 using the
scale_(x/y)_(continous/discrete)
In this example we control the continuous sale on the x-axis by providing a name, X-axis limits, the positions of breaks (ticks/labels) and the labels to place at breaks.
pcPlot +
geom_point() +
facet_grid(Smokes~Sex)+
scale_x_continuous(name="height ('cm')",
limits = c(100,200),
breaks=c(125,150,175),
labels=c("small","justright","tall"))
Similary control over discrete scales is shown below.
pcPlot <- ggplot(data=patients_clean,aes(x=Sex,y=Height))
pcPlot +
geom_violin(aes(x=Sex,y=Height)) +
scale_x_discrete(labels=c("Girls","Guys"))
Multiple X/Y scales can be combined to give full control of axis marks.
pcPlot <- ggplot(data=patients_clean,aes(x=Sex,y=Height,fill=Smokes))
pcPlot +
geom_violin(aes(x=Sex,y=Height)) +
scale_x_discrete(labels=c("Guys","Girls"))+
scale_y_continuous(breaks=c(160,180),labels=c("Petite","Tall"))
Controlling other scales.
When using fill,colour,linetype, shape, size or alpha aesthetic mappings the scales are automatically selected for you and the appropriate legends created.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=Sex))
pcPlot + geom_point(size=4)
In the above example the discrete colours for the Sex variable was selected by default.
Manual discrete colour scale
Manual control of discrete variables can be performed using scale_aes_Of_Interest_manual with the values parameter. Additionally in this example an updated name for the legend is provided.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=Sex))
pcPlot + geom_point(size=4) +
scale_color_manual(values = c("Green","Purple"),
name="Gender")
Colorbrewer for discrete colour scale
Here we have specified the colours to be used (hence the manual) but when the number of levels to a variable are high this may be impractical and often we would like ggplot2 to choose colours from a scale of our choice.
The brewer set of scale functions allow the user to make use of a range of palettes available from colorbrewer.
- Diverging
BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral
- Qualitative
Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3
- Sequential
Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=Pet))
pcPlot + geom_point(size=4) +
scale_color_brewer(palette = "Set2")## Warning: Removed 5 rows containing missing values (geom_point).
For more details on palette sizes and styles visit the colorbrewer website and ggplot2 reference page.
Continous colour scales
So far we have looked a qualitative scales but ggplot2 offers much functionality for continuous scales such as for size, alpha (transparancy), colour and fill.
scale_alpha_continuous() - For Transparancy
scale_size_continuous() - For control of size.
Both these functions accept the range of alpha/size to be used in plotting.
Below the range of alpha to be used in plot is limited to between 0.5 and 1
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,alpha=BMI))
pcPlot + geom_point(size=4) +
scale_alpha_continuous(range = c(0.5,1))
Below the range of sizes to be used in plot is limited to between 3 and 6
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point(alpha=0.8) +
scale_size_continuous(range = c(3,6))
The limits of the scale can also be controlled but it is important to note data outside of scale is removed from plot.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3,6), limits = c(25,40))## Warning: Removed 23 rows containing missing values (geom_point).
What points of scale to be labeled and labels text can also be controlled.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3,6) ,breaks=c(25,30),labels=c("Good","Good but not 25"))
Control of colour/fill scales can be best achieved through the gradient subfunctions of scale.
scale_(colour/fill)_gradient - 2 colour gradient (eg. low to high BMI)
scale_(colour/fill)_gradient2 - Diverging colour scale with a midpoint colour (e.g. Down, No Change, Up)
Both functions take a common set of arguments:-
- low - Colour for low end of gradient scale
- high - Colour for high end of gradient scale.
- na.value - Colour for any NA values.
An example using scale_colour_gradient below sets the low and high end colours to White and Red respectively
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) +
scale_colour_gradient(low = "White",high="Red")
Similarly we can use the scale_colour_gradient2 function which allows for the specification of a midpoint value and its associated colour.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) +
scale_colour_gradient2(low = "Blue",mid="Black",high="Red",midpoint = median(patients_clean$BMI))
As with previous continous scales, limits and custom labels in scale legend can be added.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) + scale_colour_gradient2(low = "Blue",
mid="Black",
high="Red",
midpoint = median(patients_clean$BMI),
breaks=c(25,30),labels=c("Low","High"),
name="Body Mass Index")
Multiple scales may be combined to create high customisable plots and scales
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI,shape=Sex))
pcPlot + geom_point(size=4,alpha=0.8)+ scale_shape_discrete(name="Gender") +scale_colour_gradient2(low = "Blue",mid="Black",high="Red",midpoint = median(patients_clean$BMI),
breaks=c(25,30),labels=c("Low","High"),
name="Body Mass Index")
Statistical transformations.
In ggplot2 many of the statistical transformations are performed without any direct specification e.g. geom_histogram() will use stat_bin() function to generate bin counts to be used in plot.
An example of statistical methods in ggplot2 which are very useful include the stat_smooth() and stat_summary() functions.
The stat_smooth() function can be used to fit a line to the data being displayed.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))
pcPlot+geom_point()+stat_smooth()## `geom_smooth()` using method = 'loess'
By default a “loess” smooth line is plotted by stat_smooth. Other methods available include lm, glm,gam,rlm.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))
pcPlot+geom_point()+stat_smooth(method="lm")
A useful feature of ggplot2 is that it uses previously defined grouping when performing smoothing.
If colour by Sex is an aesthetic mapping then two smooth lines are drawn, one for each sex.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height,colour=Sex))
pcPlot+geom_point()+stat_smooth(method="lm")
This behaviour can be overridden by specifying an aes within the stat_smooth() function and setting inherit.aes to FALSE.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height,colour=Sex))
pcPlot+geom_point()+stat_smooth(aes(x=Weight,y=Height),method="lm",inherit.aes = F)
Another useful method is stat_summary() which allows for a custom statistical function to be performed and then visualised.
The fun.y parameter specifies a function to apply to the y variables for every value of x.
In this example we use it to plot the quantiles of the Female and Male Height data
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Sex,y=Height))+geom_jitter()
pcPlot+stat_summary(fun.y=quantile,geom="point",colour="purple",size=8)
Themes
Themes specify the details of data independent elements of the plot. This includes titles, background colour, text fonts etc.
The graphs created so far have all used the default themes ,theme_grey(), but ggplot2 allows for the specification of theme used.
Predefined themes
Predefined themes can be applied to a ggplot2 object using a family of functions theme_style()
In the example below the minimal theme is applied to the scatter plot seen earlier.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme_minimal()
Several predifined themes are available within ggplot2 including:
theme_bw
theme_classic
theme_dark
theme_gray
theme_light
theme_linedraw
theme_minimal
Packages such as ggthemes also contain many useful collections of predined theme_style functions.
Creating your themes
As well as making use of predifened theme styles, ggplot2 allows for control over the attributes and elements within a plot through a collection of related functions and attributes.
theme() is the global function used to set attributes for the collections of elements/components making up the current plot.
Within the theme functions there are 4 general graphic elements which may be controlled:-
- rect
- line
- text
- title
and 5 groups of related elements:-
- axis
- legend
- strip
- panel (plot panel)
- plot (Global plot parameters)
These elements may be specified by the use of their appropriate element functions including:
- element_line()
- element_text()
- element_rect()
and additionally element_blank() to set an element to “blank”
A detailed description of controlling elements within a theme can be seen at the ggplot2 vignette and by typing ?theme into the console.
To demonstrate customising a theme, in the example below we alter one element of theme. Here we will change the text colour for the plot.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme(
text = element_text(colour="red"),
axis.text = element_text(colour="red")
)
- Note because we are changing a text element we use the element_text() function.
A detailed description of which elements are available and their associated element functions can be found by typing ?theme.
If we wished to set the y-axis label to be at an angle we can adjust that as well.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme(
text = element_text(colour="red"),
axis.text = element_text(colour="red"),
axis.title.y = element_text(angle=0)
)
Finally we may wish to remove axis line, set the background of plot panels to be white and give the strips (title above facet) a cyan background colour.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+
geom_point()+
facet_grid(Sex~Smokes)
pcPlot+theme(
text = element_text(colour="red"),
axis.text = element_text(colour="red"),
axis.title.y = element_text(angle=0),
axis.line = element_line(linetype = 0),
panel.background=element_rect(fill="white"),
strip.background=element_rect(fill="cyan")
)
+ and %+replace%
When altering themes we have been using the + operator to add themes as we would adding geoms,scales and stats.
When using the + operator
Themes elements specified in new scheme replace elements in old theme
Theme elements in the old theme which have not been specified in new theme are maintained.
This makes the + operator useful for building up from old themes.
In the example below, we maintain all elements set by theme_bw() but overwrite the theme element attribute of the colour of text.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+
theme_bw()+
theme(text = element_text(colour="red"))
The consequence can be seen comparing the effect of theme() on a plot with a default theme or theme_minimal.
Since the default theme, theme_grey() contains a specification for axis.text colour, i will not replace it with “+” operator.
pcPlot+
theme(text = element_text(colour="red"))
pcPlot+
theme_minimal()+
theme(text = element_text(colour="red"))
In contrast %+replace% replaces all elements within a theme regardless of whether they have been previously specfied in old theme.
When using the %+replace% operator
Theme elements specified in new scheme replace elements in old theme
Theme elements in the old theme which have not been specified in new theme are also replaced by blank theme elements.
oldTheme <- theme_bw()
newTheme_Plus <- theme_bw() +
theme(text = element_text(colour="red"))
newTheme_Replace <- theme_bw() %+replace%
theme(text = element_text(colour="red"))
oldTheme$text## List of 11
## $ family : chr ""
## $ face : chr "plain"
## $ colour : chr "black"
## $ size : num 11
## $ hjust : num 0.5
## $ vjust : num 0.5
## $ angle : num 0
## $ lineheight : num 0.9
## $ margin :Classes 'margin', 'unit' atomic [1:4] 0 0 0 0
## .. ..- attr(*, "valid.unit")= int 8
## .. ..- attr(*, "unit")= chr "pt"
## $ debug : logi FALSE
## $ inherit.blank: logi TRUE
## - attr(*, "class")= chr [1:2] "element_text" "element" newTheme_Plus$text## List of 11
## $ family : chr ""
## $ face : chr "plain"
## $ colour : chr "red"
## $ size : num 11
## $ hjust : num 0.5
## $ vjust : num 0.5
## $ angle : num 0
## $ lineheight : num 0.9
## $ margin :Classes 'margin', 'unit' atomic [1:4] 0 0 0 0
## .. ..- attr(*, "valid.unit")= int 8
## .. ..- attr(*, "unit")= chr "pt"
## $ debug : logi FALSE
## $ inherit.blank: logi FALSE
## - attr(*, "class")= chr [1:2] "element_text" "element" newTheme_Replace$text## List of 11
## $ family : NULL
## $ face : NULL
## $ colour : chr "red"
## $ size : NULL
## $ hjust : NULL
## $ vjust : NULL
## $ angle : NULL
## $ lineheight : NULL
## $ margin : NULL
## $ debug : NULL
## $ inherit.blank: logi FALSE
## - attr(*, "class")= chr [1:2] "element_text" "element"This means that %+replace% is most useful when creating new themes.
theme_get and theme_set
Adding titles for plot and labels.
So far no plot titles have been specified. Plot titles can be specified using the labs functions.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+labs(title="Weight vs Height",y="Height (cm)")
or specified using the ggtitle and xlab/ylab functions.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight))+geom_point()
pcPlot+ggtitle("Weight vs Height")+ylab("Height (cm)")
Saving plots
Plots produced by ggplot can be saved from the interactive viewer as with standard plots.
The ggsave() function allows for additional arguments to be specified including the type, resolution and size of plot.
By default ggsave() will use the size of your current graphics window when saving plots so it may be important to specify width and height arguments desired.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
ggsave(pcPlot,filename = "anExampleplot.png",width = 15,height = 15,units = "cm")